Reducing Nuisance Alarms

ABSTRACT

Embodiments disclosed herein may describe systems and methods for reducing nuisance alarms using probability and/or accuracy of a measured physiological parameter, such as the pulse rate or SpO2 measurement generated by a pulse oximeter. Embodiments may include methods for adjusting a predetermined alarm threshold based on the probability distribution of the estimated pulse rate and/or oxygen saturation of a patient&#39;s blood.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.61/041,042, filed Mar. 31, 2008, and is incorporated herein by referencein its entirety.

BACKGROUND

In medicine, a plethysmograph is an instrument that measuresphysiological parameters, such as variations in the size of an organ orbody part, through an analysis of the blood passing through or presentin the targeted body part or a depiction of these variations. Anoximeter is an instrument that determines the oxygen saturation of theblood. One common type of oximeter is a pulse oximeter, which determinesoxygen saturation by analysis of an optically sensed plethysmograph.

A pulse oximeter is a medical device that indirectly measures the oxygensaturation of a patient's blood (as opposed to measuring oxygensaturation directly by analyzing a blood sample taken from the patient)and changes in blood volume in the skin. Ancillary to the blood oxygensaturation measurement, pulse oximeters may also be used to measure thepulse rate of the patient.

A pulse oximeter typically includes a light sensor that is placed at asite on a patient, usually a fingertip, toe, forehead or earlobe, or inthe case of a neonate, across a foot. Light, which may be produced by alight source integrated into the pulse oximeter, containing both red andinfrared wavelengths is directed onto the skin of the patient and thelight that passes through the skin is detected by the sensor. Theintensity of light in each wavelength is measured by the sensor overtime. The graph of light intensity versus time is referred to as thephotoplethysmogram (PPG) or, more commonly, simply as the “pleth.” Fromthe waveform of the PPG, it is possible to identify the pulse rate ofthe patient and when each individual pulse occurs. In addition, bycomparing the intensities of two wavelengths at different points in thepulse cycle, it is possible to estimate the blood oxygen saturation ofhemoglobin in arterial blood. This relies on the observation that highlyoxygenated blood will absorb relatively more red light and less infraredlight than blood with lower oxygen saturation.

SUMMARY

This disclosure describes systems and methods for reducing nuisancealarms using probability and/or accuracy of a measured physiologicalparameter, such as the SpO2 and/or pulse rate measurement generated by apulse oximeter. As discussed in greater detail below, the disclosuredescribes methods for adjusting a predetermined alarm threshold based onthe probability distribution of the estimated oxygen saturation of apatient's blood and further describes methods for delaying signaling analarm based on the accuracy of the estimated oxygen saturation. In oneaspect, the disclosure describes a method for generating an alarmindicating that a physiological parameter has exceeded a predeterminedthreshold. The method includes calculating a probability distribution ofan actual value of the physiological parameter based on data receivedfrom a sensor or detector. The method then identifies the predeterminedthreshold associated with the physiological parameter and generates thealarm based on the predetermined threshold and the probabilitydistribution.

In another aspect, the disclosure describes another method forgenerating an alarm indicating that a physiological parameter hasexceeded a predetermined threshold. The method includes calculating afirst estimated value of the physiological parameter and a firstaccuracy of the first estimated value from data. The method thendetermines that the first estimated value of the physiological parameterexceeds the predetermined threshold. However, instead of generating analarm immediately, the method delays generating the alarm based on thefirst accuracy. The first accuracy is used to calculate a delay periodafter which, if the physiological parameter still exceeds thepredetermined threshold, the alarm is generated.

In yet another aspect the disclosure describes a pulse oximeter thatincludes an oxygen saturation module capable of calculating an estimatedvalue of oxygen saturation of a patient's blood from informationreceived from a sensor; an accuracy module capable of calculating theaccuracy of the estimated value; and an alarm module capable ofgenerating an alarm based on the estimated value and a predeterminedalarm threshold, wherein the alarm module is capable of delaying thegeneration of an alarm based on the accuracy of the estimated value. Thepulse oximeter may also include a probability distribution modulecapable of calculating a probability distribution of an actual value ofthe oxygen saturation of a patient's blood from information receivedfrom the sensor. In this embodiment, the alarm module is further capableof calculating an adjusted threshold based on the probabilitydistribution and the predetermined threshold and generating an alarmbased on the adjusted threshold.

The disclosure also describes a combined method for generating an alarmin which an adjusted threshold is calculated and if that threshold isexceeded, a delay period may be used to delay the generation of analarm. For example, an embodiment of such a combined method includes theoperations: a) receiving current data indicative of the physiologicalparameter; b) calculating an estimated value of the physiologicalparameter and an accuracy of the estimated value based on the currentdata; c) calculating a probability distribution of an actual value ofthe physiological parameter based on the current data; d) calculating anadjusted threshold based on the predetermined threshold and theprobability distribution; e) determining that the estimated value of thephysiological parameter exceeds the adjusted threshold; and f) delayinggenerating the alarm for at least a first time period based on acomparison of the accuracy with a predetermined accuracy range. Afterdelaying generating the alarm for at least a first time period,operations a), b) and e) may be repeated and the alarm then generated.Alternatively, operations a)-e) may be repeated.

These and various other features as well as advantages whichcharacterize the disclosed systems and methods will be apparent from areading of the following detailed description and a review of theassociated drawings. Additional features of the systems and methodsdescribed herein are set forth in the description which follows, and inpart will be apparent from the description, or may be learned bypractice of the technology. The benefits and features will be realizedand attained by the structure particularly pointed out in the writtendescription and claims as well as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the disclosed technology asclaimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawing figures, which form a part of this application,are illustrative of disclosed technology and are not meant to limit thescope of the description in any manner, which scope shall be based onthe claims appended hereto.

FIG. 1 is a perspective view of a pulse oximetry system, according to anembodiment.

FIG. 2 is a block diagram of the exemplary pulse oximetry system of FIG.1 coupled to a patient, according to an embodiment.

FIG. 3 shows a depiction of two Gaussian bell-curves of the probabilitydistribution in which the estimated SpO₂ value is 90%, but havingaccuracies of +/−2 (solid) and +/−5 (dashed) respectively, according toan embodiment.

FIG. 4 depicts the same distributions as shown in FIG. 4 plotted as thecumulative probability that the actual SpO₂ value is below or above eachof the SpO₂ values on x-axis, according to an embodiment.

FIG. 5 illustrates the probability distributions of SpO₂ values equal tothese alarm thresholds and associated SpO₂ accuracies (+/−1 standarddeviation), according to an embodiment.

FIG. 6 illustrates an embodiment of a method for generating an alarmbased on the monitoring of a physiological parameter, according to anembodiment.

FIG. 7 illustrates an embodiment of a method for generating an alarmindicating that a physiological parameter has exceeded a predeterminedthreshold, according to an embodiment.

FIG. 8 illustrates an alternative embodiment of a method for generatingan alarm indicating that a physiological parameter has exceeded apredetermined threshold, according to an embodiment.

FIG. 9 is a block diagram illustrating some of the components of a pulseoximetry system that generates an alarm based the accuracy and/orprobability distribution of sensed data, according to an embodiment.

DETAILED DESCRIPTION

This disclosure describes systems and methods for reducing nuisancealarms using probability and/or accuracy of a measured physiologicalparameter, such as the SpO2 and/or pulse rate measurements generated bya pulse oximeter. As discussed in greater detail below, the disclosuredescribes methods for adjusting a predetermined alarm threshold based onthe probability distribution of the estimated oxygen saturation of apatient's blood and further describes methods for delaying signaling analarm based on the accuracy of the estimated oxygen saturation.

Although the techniques for generating alarms based on an estimatedphysiological parameter introduced above and discussed in detail belowmay be implemented by a variety of medical devices and for a variety ofphysiological parameters, the present disclosure will discuss theimplementation of these techniques in a pulse oximeter. Althoughdescribed in detail in this context of a pulse oximeter displayingoxygen saturation measurements, the reader will understand that thesystems and methods described herein may be equally adapted to thegeneration of alarms based on the measurement of any physiologicalparameter of any patient (human or non-human) generated by anymonitoring device.

FIG. 1 is a perspective view of an embodiment of a pulse oximetry system10. The system 10 includes a sensor 12 and a pulse oximetry monitor 14.The sensor 12 includes an emitter 16 for emitting light at one or morewavelengths into a patient's tissue. A detector 18 is also provided inthe sensor 12 for detecting the light originally from the emitter 16that emanates from the patient's tissue after passing through thetissue. The emitter 16 and detector 18 may be on opposite sides of adigit such as a finger or toe, in which case the light that is emanatingfrom the tissue has passed completely through the digit. In analternative embodiment the emitter 16 and detector 18 may be arranged sothat light from the emitter 16 penetrates the tissue and is reflected bythe tissue into the detector 18, such as a sensor designed to obtainpulse oximetry data from a patient's forehead.

The sensor may be connected to and draw its power from the monitor 14 asshown. Alternatively, the sensor may be wirelessly connected to themonitor 14 and include its own battery or similar power supply (notshown). The monitor 14 may be configured to calculate physiologicalparameters based on data received from the sensor 12 relating to lightemission and detection. Further, the monitor 14 includes a display 20configured to display the physiological parameters, other informationabout the system, and/or alarm indications. In the embodiment shown, themonitor 14 also includes a speaker 22 to provide an audible alarm in theevent that the patient's physiological parameters are not within anormal range, as defined based on patient characteristics.

The sensor 12 is communicatively coupled to the monitor 14 via a cable24. However, in other embodiments a wireless transmission device (notshown) or the like may be utilized instead of or in addition to thecable 24.

In the illustrated embodiment, the pulse oximetry system 10 alsoincludes a multi-parameter patient monitor 26. The monitor may becathode ray tube type, a flat panel display (as shown) such as a liquidcrystal display (LCD) or a plasma display, or any other type of monitornow know or later developed. The multi-parameter patient monitor 26 maybe configured to calculate physiological parameters and to provide acentral display 28 for information from the monitor 14 and from othermedical monitoring devices or systems (not shown). For example, themultiparameter patient monitor 26 may be configured to display anestimate of a patient's blood oxygen saturation generated by the pulseoximetry monitor 14 (referred to as an “SpO₂”), pulse rate informationfrom the monitor 14 and blood pressure from a blood pressure monitor(not shown) on the display 28. Additionally, the multi-parameter patientmonitor 26 may emit a visible or audible alarm via the display 28 or aspeaker 30, respectively, if the patient's physiological parameters arefound to be outside of the normal range. The monitor 14 may becommunicatively coupled to the multi-parameter patient monitor 26 via acable 32 or 34 coupled to a sensor input port or a digitalcommunications port, respectively or may communicate wirelessly (notshown). In addition, the monitor 14 and/or the multi-parameter patientmonitor 26 may be connected to a network to enable the sharing ofinformation with servers or other workstations (not shown). The monitor14 may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet.

FIG. 2 is a block diagram of the embodiment of a pulse oximetry system10 of FIG. 1 coupled to a patient 40 in accordance with presentembodiments. Specifically, certain components of the sensor 12 and themonitor 14 are illustrated in FIG. 2. The sensor 12 includes the emitter16, the detector 18, and an encoder 42. In the embodiment shown, theemitter 16 is configured to emit at least two wavelengths of light,e.g., RED and IR, into a patient's tissue 40. Hence, the emitter 16 mayinclude a RED light emitting light source such as the RED light emittingdiode (LED) 44 shown and an JR light emitting light source such as theIR LED 46 shown for emitting light into the patient's tissue 40 at thewavelengths used to calculate the patient's physiological parameters. Incertain embodiments, the RED wavelength may be generally between about600 nm and about 700 nm, and the IR wavelength may be between about 800nm and about 1000 nm.

Alternative light sources may be used in other embodiments. For example,a single wide-spectrum light source may be used, and the detector 18 maybe configured to detect light only at certain wavelengths.Alternatively, a third light source may be provided based on its abilityto obtain an accurate signal when the starting oxygen saturation isknown while consuming less power. In another example, the detector 18may detect a wide spectrum of wavelengths of light, and the monitor 14may process only those wavelengths which are of interest or which takethe least power to detect.

It should be understood that, as used herein, the term “light” may referto one or more of ultrasound, radio, microwave, millimeter wave,infrared, visible, ultraviolet, gamma ray, X-ray and/or otherelectromagnetic radiation, and may also include any wavelength withinthe radio, microwave, infrared, visible, ultraviolet, or X-ray spectra,and that any suitable wavelength of light may be appropriate for usewith the present techniques.

In an embodiment, the detector 18 may be configured to detect theintensity of light at the RED and IR wavelengths. In operation, lightenters the detector 18 after passing through the patient's tissue 40.The detector 18 converts the intensity of the received light into anelectrical signal. The light intensity is directly related to theabsorbance and/or reflectance of light in the tissue 40. That is, whenmore light at a certain wavelength is absorbed or reflected, less lightof that wavelength is received from the tissue by the detector 18. Afterconverting the received light to an electrical signal, the detector 18sends the signal to the monitor 14, where physiological parameters maybe calculated based on the absorption of the RED and IR wavelengths inthe patient's tissue 40. An examples of devices configured to performsuch calculations are the Model N600 and N600x pulse oximeters availablefrom Nellcor Puritan Bennett LLC.

In an embodiment, the encoder 42 may contain information about thesensor 12, such as what type of sensor it is (e.g., whether the sensoris intended for placement on a forehead or digit) and the wavelengths oflight emitted by the emitter 16. This information may be used by themonitor 14 to select appropriate algorithms, lookup tables and/orcalibration coefficients stored in the monitor 14 for calculating thepatient's physiological parameters. In addition the encoder 42 mayinclude information such as coefficients utilized for the calculation ofSpO2. All information in the encoder 42 may be digitally encoded toinsure accuracy, among other considerations.

In addition, the encoder 42 may contain information specific to thepatient 40, such as, for example, the patient's age, weight, anddiagnosis. This information may allow the monitor 14 to determinepatient-specific threshold ranges in which the patient's physiologicalparameter measurements should fall and to enable or disable additionalphysiological parameter algorithms. The encoder 42 may, for instance, bea coded resistor which stores values corresponding to the type of thesensor 12, the wavelengths of light emitted by the emitter 16, and/orthe patient's characteristics. These coded values may be communicated tothe monitor 14, which determines how to calculate the patient'sphysiological parameters and alarm threshold ranges. In anotherembodiment, the encoder 42 may include a memory on which one or more ofthe following information may be stored for communication to the monitor14: the type of the sensor 12; the wavelengths of light emitted by theemitter 16; the proper calibration coefficients and/or algorithms to beused for calculating the patient's physiological parameters and/or alarmthreshold values; the patient characteristics to be used for calculatingthe alarm threshold values; and the patient-specific threshold values tobe used for monitoring the physiological parameters.

Signals from the detector 18 and the encoder 42 may be transmitted tothe monitor 14. In the embodiment shown, the monitor 14 includes ageneral-purpose microprocessor 48 connected to an internal bus 50. Themicroprocessor 48 is adapted to execute software, which may include anoperating system and one or more applications, as part of performing thefunctions described herein. Also connected to the bus 50 are a read-onlymemory (ROM) 52, a random access memory (RAM) 54, user inputs 56, thedisplay 20, and the speaker 22.

The RAM 54 and ROM 52 are illustrated by way of example, and notlimitation. Any computer-readable media may be used in the system fordata storage. Computer-readable media are capable of storing informationthat can be interpreted by the microprocessor 48. This information maybe data or may take the form of computer-executable instructions, suchas software applications, that cause the microprocessor to performcertain functions and/or computer-implemented methods. Depending on theembodiment, such computer-readable media may comprise computer storagemedia and communication media. Computer storage media includes volatileand non-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EPROM, EEPROM, flash memory or other solid state memory technology,CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetictape, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store the desired information andwhich can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 provides timingcontrol signals to a light drive circuitry 60 which controls when theemitter 16 is illuminated and multiplexed timing for the RED LED 44 andthe IR LED 46. The TPU 58 also controls the gating-in of signals fromdetector 18 through an amplifier 62 and a switching circuit 64. Thesesignals are sampled at the proper time, depending upon which lightsource is illuminated. The received signal from the detector 18 may bepassed through an amplifier 66, a low pass filter 68, and ananalog-to-digital converter 70. The digital data may then be stored in aqueued serial module (QSM) 72 (or buffer) for later downloading to theRAM 54 as the QSM 72 fills up. In one embodiment, there may be multipleseparate parallel paths having the amplifier 66, the filter 68, and theAD converter 70 for multiple light wavelengths or spectra received.

The microprocessor 48 may determine the patient's physiologicalparameters, such as SpO₂ and pulse rate, using various algorithms and/orlook-up tables based on the value of the received signals and/or datacorresponding to the light received by the detector 18. Signalscorresponding to information about the patient 40, and particularlyabout the intensity of light emanating from a patient's tissue overtime, may be transmitted from the encoder 42 to a decoder 74. Thesesignals may include, for example, encoded information relating topatient characteristics. The decoder 74 may translate these signals toenable the microprocessor to determine the thresholds based onalgorithms or look-up tables stored in the ROM 52. The encoder 42 mayalso contain the patient-specific alarm thresholds, for example, if thealarm values are determined on a workstation separate from the monitor14. The user inputs 56 may also be used to enter information about thepatient, such as age, weight, height, diagnosis, medications,treatments, and so forth. In certain embodiments, the display 20 mayexhibit a list of values which may generally apply to the patient, suchas, for example, age ranges or medication families, which the user mayselect using the user inputs 56. The microprocessor 48 may thendetermine the proper thresholds using the user input data and algorithmsstored in the ROM 52. The patient-specific thresholds may be stored onthe RAM 54 for comparison to measured physiological parameters.

The embodiments described herein relate to adjusting the conditionsunder which an alarm is generated based on one or more statisticalparameters of an estimated physiological parameter value. Statisticalparameters associated with the physiological parameter includeparameters related to the accuracy of the estimated value such as errorestimates and probability distributions of the data.

The following FIGS. 3 and 4 illustrate how different accuracies ofestimates of SpO₂ values affect the probability distribution of theestimated values, according to various embodiments. FIG. 3 shows adepiction of two Gaussian bell-curves of the probability distribution inwhich the estimated SpO₂ value is 90%, but having accuracies of +/−2%(solid) and +/−5% (dashed) respectively. As shown in FIG. 3, thedifference in accuracy has a large effect on the probabilitydistribution and the probability that the actual SpO₂ value is above orbelow any particular value. For example, if the accuracy is +/−2%, thereis nearly a 100% chance that the actual value of the SpO₂ value iswithin the interval of 85% to 95%. The same can not be said of the datahaving an accuracy of +/−5%.

FIG. 4 depicts the same two contrasting examples, but shows thecumulative probability that the actual SpO₂ value is below or above eachof the SpO₂ values on x-axis. For convenience, only a portion of twodifferent sets of curves are displayed on FIG. 4. Midway between thehigh saturation alarm threshold (100% saturation) and low saturationalarm threshold (85% saturation), the curves switch from “probability ofSpO₂ below value” curves at each accuracy to “probability of SpO₂ abovevalue” curves at each accuracy. If all of the “below value” curves weredisplayed, they would start to level off and end at the point 100%probability/100% saturation. The “probability of SpO₂ above value”curves would be seen to continue to rise and then level off, approaching100% probability to end at the point 100% probability/0% saturation. Itshould also be pointed out that at the SpO₂ value of 90% the probabilitycurves converge.

This disclosure describes systems and methods for using accuracy and/orprobability to reduce the number of nuisance alarms generated due tohigh noise in the data used to estimate physiological parameters. In anembodiment, the oximeter would adapt its alarm thresholds to assure thatthe probability of SpO₂ being outside the user-set limits was highbefore an alarm would be generated.

In order to illustrate this, consider the following three examples ofestimated values of SpO₂ with different accuracies and probabilitydistributions. The first example is a simple case in which no noise ismeasured and the sensor has a presumed accuracy (SpO₂−SaO₂) of +/−2% (1standard deviation) due to physio-optics. Assuming that SpO₂−SaO₂ errorshave a Gaussian zero-mean distribution, an SpO₂ of 85% really means thatthere is a 50% probability of SaO₂<85%, an 83% probability of SaO₂<87%,a 95% probability of SaO₂<88.3%, a 97.5% probability of SaO₂<89%, etc.

In the second example, there is noise in the data, perhaps due to motionartifact, and the current level of said noise corresponds to a somewhatdegraded accuracy of +/−4% of oxygen saturation. In this situation, thatsame SpO₂ of 85% means that there is a 50% probability of SaO₂<85%, an83% probability of SaO₂<89%, a 95% probability of SaO₂<91.6%, a 97.5%probability of SaO₂<93%, etc.

The third example is an extremely challenging case in which the accuracyis degraded to +/−8%, in which case an SpO₂ of 85% means that there is a50% probability of SaO₂<85%, an 83% probability of SaO₂<93%, a 95%probability of SaO₂<98.2%, etc.

In embodiments described herein, different alarm thresholds are used tocompensate for the different noise in order to reduce the instances ofnuisance alarms due to noise. In an embodiment, this is done bydesigning the oximeter to alarm when the SpO₂, together with said noisemetrics as indicated by the probability distribution, indicates a 95%probability that the SaO₂ is lower than 88.3% (set low saturation alarmthreshold of 85 plus a 3.3% margin to reflect the probabilitydistribution associated with a nominal 2% accuracy). In the firstexample above, the oximeter would alarm for estimated values ofSpO₂<85%, but in the 2nd noisier example, it would alarm only forestimated values of SpO₂<81.7%, and in the third example thisprobability-enhanced oximeter would alarm only for estimated values ofSpO₂<75.1%.

FIG. 5 illustrates the probability distributions of SpO₂ values equal tothese alarm thresholds and associated SpO₂ accuracies (+/−1 standarddeviation), according to an embodiment. In each case, the integral underthe curve and above the SpO₂ alarm threshold of 88.3% (85%+3.3% margin)is 5%, although probability distributions differ substantially. Thismeans that the probability that SaO₂ is NOT under this 88.3% thresholdprobability of false alarm) is only 5% in each case. In contrast, if thelow SpO₂ alarm limit were to remain fixed at 85%, then in the +/−8 case,an SpO₂ at this fixed limit of 85% would be associated with about a 40%probability of SaO₂≧88.3%, with a corresponding likelihood of falsealarms.

In an alternative embodiment, at times when said noise metrics increasesuddenly and dramatically, probability theory may be used to hold theolder and presumably more accurate SpO₂ value, instead of displaying thenewer but less accurate value, for some period of time determined basedon the noise. For example, if an increase in said noise metricsindicates that SpO₂ accuracy has just degraded from +/−2 points to +/−8points, the oximeter might be designed to hold the old SpO₂ value unlessthe new SpO₂ value has changed by 8 points or until the noise metrics goback down. Alternatively, it could assume that the accuracy of the heldSpO₂ value might degrade at some fixed rate, such as for example 0.5%per second, and therefore hold the old SpO₂ value for no longer than8.0/0.5=16 seconds.

Either the dynamic alarm threshold or holding scheme based on noise(signal quality) metrics in combination with probability significantlyreduces nuisance alarms. Furthermore, a time-value integral may be usedto reduce nuisance alarms. This method may integrate at a rate that wasmodified (reduced) by the probability that the estimated value exceededthe alarm threshold. It should be noted that, to the extent that thechallenging conditions that degrade oximeter accuracy are brief induration, modifying the oximeter's behavior to assure a higherprobability that the alarms are real should preserve patient safety, asmotion artifact generally biases SpO₂ low. Similarly, high SpO₂ alarms,to the extent that they are reduced or delayed in neonates, will be lessurgent than low SpO₂ alarms, as they impact longer-term developmentrather than the body's ability to meet current metabolic demands.

FIG. 6 illustrates an embodiment of a method for generating an alarmbased on the monitoring of a physiological parameter. In the embodimentshown, an alarm will be generated when the monitored physiologicalparameter is determined to meet an alarm condition. For the purposes ofthis disclosure, the alarm condition will be discussed in terms of avalue of some parameter exceeding a threshold value. One skilled in theart will understand that when a value is said to “exceed a threshold” itmeans that the value is within a range of values for which an alarmshould be generated; the range of values being separated by thethreshold from an acceptable range of values for which an alarm will notbe generated. Thus, the term “exceeding a threshold” covers both theinstance in which a measured or calculated value is lower than a lowerthreshold of an acceptable range and the instance in which the measuredor calculated value is higher that an upper threshold of the acceptablerange.

In the method 600, information and/or data are received from a datasource in a receive data operation 602. In the context of the pulseoximeter described above, the receive data operation 602 includesreceiving an electronic signal from a sensor indicative of the lightreceived by the detector and processing that signal to generate datathat can be processed by the microprocessor. In an embodiment, the data,or samples thereof, may be temporarily stored in a buffer to allowmultiple analyses of the same data. In additional, some or all of thedata may be retained for some period of time for subsequent processingat a later time.

The received data are analyzed to generate an estimated value of thephysiological parameter that is being measured in the generate estimateoperation 604. In the context of a pulse oximeter, one physiologicalparameter estimated is the oxygen saturation of the blood of thepatient. In an embodiment, the oxygen saturation of the patient's bloodis calculated based on the most recent data received from the sensor inorder to provide a current measurement of the oxygen saturation. Theestimated value of the oxygen saturation may be generated by asophisticated algorithm and may utilize a significant amount ofprocessor cycles and signal processing hardware (e.g., filters,amplifiers, high-resolution digital to analog converters, etc.). In anembodiment, generation of the estimated value of the oxygen saturationmay use statistical information derived from data in the generation ofthe final estimate. An example of generating an estimated value foroxygen saturation is described in the commonly-assigned U.S. Pat. No.6,836,679, titled “Method and apparatus for estimating physiologicalparameters using model-based adaptive filtering”. Other methods forgenerating the estimated value are known in the art and any suitablemethod, now known or later developed, may be used.

In addition to the generation of the estimated value, one or morestatistical parameters describing the data are calculated in a calculatestatistical parameters operation 606. In the embodiment shown, thecalculate statistical parameters operation 606 is performed after thegenerate estimate operation 604. In alternative embodiments, thestatistical parameter(s) may be calculated before, concurrently with oras part of the generate estimate operation 604. In yet anotherembodiment, the calculate statistical parameters operation 606 may beperformed only if the estimated value exceeds a predetermined (i.e.,non-adjusted) alarm condition.

Furthermore, the statistical parameter(s) calculated may or may not beused in the calculation of the estimated value and may or may not becalculated by the same module, software application or component of thepulse oximeter that calculates the estimated value. For example, in anembodiment, a single software application may be utilized to calculateall parameters. In an alternative embodiment, separate and independentsoftware modules or system components may be used to calculate eachparameter described herein.

One statistical parameter that may be determined from the data is asingle value representing the accuracy of the estimated value. Accuracyis a well known statistical term of art referring how close an estimatedvalue is likely to be to the actual value based on the errors andlimitations of the measurement process and the data obtained. Accuracycan be quantified by many different techniques, including reporting asingle value that is a numerical representation of the accuracy of theestimated value. For example, the accuracy of an estimated value ofoxygen saturation may be determined by calculating a standard deviationof the physiological data from which the estimated value is determined;the smaller the standard deviation, the greater the accuracy of theestimated value. Thus, the standard deviation itself may be displayed asa parameter representing the accuracy of the estimated value.

In an alternative embodiment, the accuracy an estimated value may befurther determined by more complicated calculations involving a detailedanalysis of data received from the sensor. A number of datacharacteristics potentially indicative of the accuracy of pulse oximetrycalculations (i.e. oxygen saturation and pulse rate) are know to thoseskilled in the art. Examples include pulse amplitude, pulse shape,correlation of the IR and Red photoplethysmograph, as well as temporalvariations in any of these characteristics. Those skilled in the art ofpulse oximeter design will also recognize that multiple datacharacteristics may be combined empirically in order to more accuratelyreflect the accuracy of a pulse oximetry system under challengingconditions that may be created experimentally or encountered in clinicalusage. Those skilled in the art will also recognize that in order todesign such an empirical combination, the accuracy of oxygen saturationand pulse rate measurements under challenging conditions may beindependently assessed by techniques such as arterial blood sampling,ECG monitoring, or use of a second pulse oximeter at a different tissuesite. Such empirical combinations of data characteristics reflective ofthe accuracy of a physiological parameter may involve linear ornon-linear combinations, together with one or more thresholds, limits,coefficients, exponents, etc. The process of defining such an empiricalcombination of data characteristics, including gathering oximetry datarepresentative of a variety of challenging conditions, may betime-consuming and complex, but would nevertheless be a routineundertaking for one of ordinary skill in the art.

Statistical parameters calculated in the calculate statisticalparameters operation 606 may also include parameters based onprobability distribution of the data. This may include selecting aprobability profile (e.g., Gaussian) and matching the data to theprofile in order to determine the statistical parameters.

An example of one such probability distribution statistical parameter isa confidence interval. A confidence interval is an interval estimate ofa parameter. The confidence interval represents a range of values withinwhich the actual value of the parameter is expected to occur with a highprobability, referred to as the confidence level. For example, aconfidence interval may be a range within which the actual value isexpected to be with a 95% certainty. An alternative way of stating thisis that there is a 95% probability that the actual value will be withinthe confidence interval.

When calculating confidence intervals, the confidence level to be usedis identified as part of the operation 606. The confidence level may bepredetermined by the system manufacturer, selected automatically by thesystem, or may be adjustable by the user of the system. For example, inan embodiment a system may use a 95% confidence level for all confidenceinterval calculations unless a user has specifically selected adifferent confidence level. The system may facilitate user selection ofconfidence levels by providing an interface, such as a confidence levelselection menu, through which the user can select a confidence level(e.g., 90%, 95%, 97.5%, 98%, 99%, etc.) or enter a user-designatedconfidence level. Confidence levels received by the system from a userthrough such an interface may be stored in memory on the system and alsomay be displayed on the display GUI as described in greater detailbelow.

Confidence intervals may also be represented as confidence limits, suchas an upper confidence limit and a lower confidence limit. A confidencelimit is an interval estimate of a parameter indicating the probabilitythat the actual value is above (or below) the limit. For the purposes ofthis disclosure, the terms “upper confidence limit” or “upper limit”refers to the upper limit of any confidence interval (including aconfidence interval with no lower limit) and, likewise, the term “lowerconfidence limit” will be used when referring to the lower limit of anyconfidence interval with a lower limit. The relationship between theestimated value, its accuracy estimate, and its confidence intervaldepends on the probability distribution function of the measurementerrors. Measurement errors are often presumed to have a Gaussianprobability distribution, characterized by the “bell-curve” functione^(−(x/σ)) ² , where C denotes the standard deviation of the measurementerrors (i.e. the accuracy). Assuming a Gaussian error distribution, anoxygen saturation estimate of 92% with an accuracy, σ, of 3% will have a95% confidence interval of 92%±6%, or 86%-98%, because 95% of the areaunder the curve defined by a Gaussian error distribution falls withinthe range of ±2σ.

Confidence intervals, confidence limits and accuracy calculations areexamples of statistical parameters that may be calculated by the method600. The reader will understand that any other statistical parameterrelated to any aspect of the data (e.g., signal strength, noise,harmonics, etc.) may be calculated as part of the calculate statisticalparameter operation 606.

After the calculations have been made, an alarm condition adjustmentoperation 608 is performed. In the alarm condition adjustment operation608 an adjusted alarm condition is determined based on a predeterminedalarm condition. In an embodiment, the predetermined alarm condition maybe defined by a predetermined acceptable range that is defined by twopredetermined thresholds. The predetermined thresholds may beuser-selected thresholds or thresholds provided by the devicemanufacturer. The predetermined thresholds may be provided in terms ofestimated value thresholds or in some other form, such as confidencelimit thresholds.

The alarm condition adjustment operation 608 retrieves or otherwiseaccesses the predetermined alarm condition and then adjusts it, ifnecessary, based on the statistical parameters. In an embodiment, thepredetermined alarm condition may be adjusted based on accuracy,probability distribution, or any other statistical parameter. Theadjustment may include changing the value of a predetermined threshold(e.g., raising the threshold or lowering it), changing the type ofthreshold (e.g., changing a predetermined estimated value threshold to athreshold for a confidence limit) and/or changing a temporal condition(such as a delay period) that must be met in addition to some othercondition such as the estimated value exceeding a threshold.

In the embodiment shown, the alarm condition adjustment operation 608 isillustrated as being performed after calculation of the estimated valueand statistical parameters. In an alternative embodiment, the alarmcondition adjustment operation 608 may be performed concurrently with oras part of the calculate statistical parameter(s) operation 606. In yetanother embodiment, the alarm condition adjustment operation 608 may beperformed only if the estimated value exceeds the non-adjusted alarmcondition.

The alarm condition adjustment operation 608 may be omitted in anembodiment in which the predetermined threshold is defined in terms of aconfidence level rather than in terms of an estimated value. Forexample, in an embodiment a predetermined threshold may be defined as a95% confidence limit threshold of SaO2≧85% oxygen saturation. In thisexample, if the data indicate that there is 95% probability that theactual SaO2≧83%, the threshold is exceeded and the alarm should begenerated. In this embodiment, the alarm is generated based on thepredetermined threshold and the probability distribution without theneed to adjust the alarm condition.

A generate alarm operation 610 is then performed in which the adjustedalarm condition is used to determine if an alarm should be generated(i.e., an audio alarm should be sounded, a visual alarm shoulddisplayed, an alarm notification should be sent, etc.). The generatealarm operation 610 includes testing the adjusted alarm conditionagainst the current data and calculated values in order to determine ifthe adjusted alarm condition has been met. If the condition is met, analarm is generated. An alarm may be generated until the measured dataindicate that the adjusted alarm condition is no longer met, until auser resets the device, or until some other interrupt occurs.

In an embodiment, the adjusted alarm condition may include an adjustedthreshold for a specific parameter (e.g., an accuracy, confidenceinterval, estimated value, etc.). The generate alarm operation 610compares the adjusted threshold to the appropriate parameter todetermine if the parameter exceeds the adjusted threshold (i.e., isoutside of the acceptable range). If so, the alarm is generated. In analternative embodiment, an adjusted alarm condition may include atemporal requirement (e.g., the estimated value must exceed thethreshold for some period of time determined based on a statisticalparameter) before the adjusted alarm condition is considered to be met.These and other embodiments of the adjusted alarm condition arediscussed in greater detail below.

In an embodiment, the method 600 is performed continuously, which isillustrated by the flow returning to the receive data operation 602.That is, data is continually being received, calculations are made fromthe most recent data and the alarm conditions are continuously tested.Depending on the implementation, this can be done in a true continuousprocess or can be done by periodically repeating the operations in FIG.6 for batches of data received and revising the displayed values aftereach repetition. For example, the method 600 may be performed twiceevery second.

FIG. 7 illustrates an embodiment of a method for generating an alarmindicating that a physiological parameter has exceeded a predeterminedthreshold. In the embodiment shown, the method starts with thecollection of data indicative of the physiological parameter from thepatient in a data collection operation 702. In the context of a pulseoximeter, the data is received from a sensor and may take the form of astream of data indicative of different light intensities measured by adetector as described above.

Next, a calculate estimated value operation 704 is performed. Theestimated value of the physiological parameter being measured iscalculated from the data received.

In addition, a calculate probability distribution operation 706 isperformed. In an embodiment this operation 706 calculates a probabilitydistribution of the actual value of the physiological parameter based onthe data received. As discussed above, the calculate distributionoperation 706 may include calculating an accuracy of the estimatedvalue. It may also include calculating a confidence interval or aconfidence limit based on a predetermined confidence level, which may beretrieved from memory as part of the operation. The confidence level maybe predetermined by the manufacturer (e.g., selected from 99%, 98%,97.5%, 95%, 90%) or may be a user-selected value.

The calculate probability distribution operation 706 may be performedindependently of the calculate estimated value operation 704 asillustrated. Alternatively, the two operations may be combined into asingle operation. In addition, as discussed above the calculateprobability distribution operation 706 may be performed automatically oronly after a predetermined alarm condition has been met.

In the embodiment shown, a predetermined threshold is determined in anidentify threshold operation 708. In the operation 708, a threshold maybe retrieved from memory accessible by the system. This may includedetermining which predetermined threshold should be used based on otherinformation such as patient characteristics, physiological parameterbeing monitored, etc.

A calculate adjusted threshold operation 710 is then performed. In thecalculate adjusted threshold operation 710 the predetermined thresholdis modified based on the probability distribution. In the embodimentshown, the predetermined threshold may be provided in terms of anestimated value threshold and the adjustment operation 710 includescalculating an adjusted estimated value threshold based on theprobability distribution.

For example, in an embodiment the predetermined threshold may be a lowerthreshold limit of SpO₂=85% oxygen saturation. Due to an increasedamount of noise (e.g., resulting an accuracy of +/−4 as described above)that changes the probability distribution of the estimated SpO₂ values,the predetermined threshold may be lowered to generate an adjustedthreshold of SpO₂=81.7%. This results in an adjusted threshold for SpO₂that maintains the ≦5% probability of a false alarm at the decreasedaccuracy.

A generate alarm operation 712 is then performed in which the adjustedthreshold is used to determine if an alarm should be generated (i.e., anaudio alarm should be sounded, a visual alarm should displayed, an alarmnotification should be sent, etc.). The generate alarm operation 712includes testing the adjusted threshold against the current data andcalculated values in order to determine if the adjusted alarm conditionhas been met. If the threshold is exceeded, an alarm is generated.

As part of testing to determine if the adjusted alarm condition is met,the generate alarm operation 712 may compare the adjusted threshold tothe appropriate parameter to determine if the parameter exceeds theadjusted threshold (i.e., is outside of the acceptable range). If so,the alarm is generated.

In an embodiment, the method 700 is performed continuously, which isillustrated by the flow returning to the receive data operation 702.That is, data is continually being received, calculations are made fromthe most recent data and the displayed values are continuously updated.Depending on the implementation, this can be done in a true continuousprocess or can be done by periodically repeating the operations in FIG.7 for batches of data received and revising the displayed values aftereach repetition.

FIG. 8 illustrates an alternative embodiment of a method for generatingan alarm indicating that a physiological parameter has exceeded apredetermined threshold. In the embodiment shown, the method starts withthe collection of data indicative of the physiological parameter fromthe patient in a data collection operation 802. In the context of apulse oximeter, the data are received from a sensor and may take theform of a stream of data indicative of different light intensitiesmeasured by a detector as described above.

Next, a calculate estimated value operation 804 is performed. Theestimated value of the physiological parameter being measured iscalculated from the data received.

In addition, a calculate accuracy operation 806 is performed. In anembodiment, this operation 806 calculates the accuracy of the estimatedvalue of the SpO₂ based on the data received. The calculate accuracyoperation 806 may be performed independently of the calculate estimatedvalue operation 804 as illustrated. Alternatively, the two operationsmay be combined into a single operation. In addition, as discussed abovethe calculate accuracy operation 806 may be performed automatically oronly after a predetermined alarm condition has been met.

The method further performs a first determination operation 808 thatdetermines if the predetermined alarm threshold has been exceeded. Inthe embodiment shown, this includes retrieving a predetermined SpO₂threshold and comparing the threshold with the current estimated valueof SpO₂. Alternatively, a different type of threshold (e.g., aconfidence threshold or an adjusted threshold) may be retrieved asdescribed above. If the threshold has not been exceeded, the normalmonitoring and analysis continues as illustrated by the operational flowreturning to the receive data operation 802.

If the threshold has been exceeded, then the accuracy is evaluated in asecond determination operation 810. The purpose of the seconddetermination operation 810 is to determine if the accuracy of themeasurements and calculations being performed has recently degraded,thus indicating that the threshold may have been exceeded not because ofa change in the physiological parameter but rather due to inaccurateestimation. In an embodiment, the second determination operation 810compares the calculated accuracy of the current estimated value to oneor more recently calculated accuracies. If there has not been asignificant change between the current accuracy and the previousaccuracy data (e.g., a difference of >+/−1% oxygensaturation, >+/−2%, >+/−3% or >+/−4% between the current and recentaccuracies, for example), then the method 800 causes the alarm to begenerated in a generate alarm operation 816, In order to facilitate thisanalysis, recent values of accuracies may be stored in temporary memoryfor the purpose of comparing them with accuracies calculated in thefuture. But if the accuracy has changed enough to meet the requirementsof the second determination operation 810, a generate delay periodoperation 812 is performed.

In an alternative embodiment, the significance of the current accuracymay be based on a comparison with a predetermined acceptable accuracyrange. This range may be determined by the manufacturer or selected bythe user. For example, an acceptable range of +/−1%, +/−1.5% or +/−2%may be used as the predetermined acceptable range for accuracy withinwhich any exceeding of the threshold will be automatically considered tobe a true alarm condition. Thus, if the accuracy of the current estimateis +/−1.5% it would be considered to be within an acceptable range of,for example, +/−2% and the alarm is generated. If the accuracy isgreater than the acceptable range, the generate delay period operation812 may be performed.

In yet another embodiment, the second determination operation 810 may beomitted altogether. In this embodiment, any time a threshold is exceededthe method proceeds directly to the generate delay period operation 812regardless of the accuracy of the current estimated value of thephysiological parameter.

The generate delay period operation 812 identifies a delay period whichis used to delay the generation of the alarm. One purpose of the delayperiod is to allow for the noise to subside and the accuracy to improve,in the event that the alarm is being triggered by noise and not actualchanges in the measured physiological parameter. In another embodiment,a time-value integral may be used to reduce nuisance alarms and mayintegrate at a rate that is modified (reduced) by the probability thatthe estimated value exceeded the alarm threshold, such as described inU.S. Pat. No. 5,865,736.

In an embodiment, the delay period may be determined based on thecalculated accuracy of the current estimate. Such a calculation may bebased solely on the accuracy of the current estimate (as describedabove) or may be determined based on a comparison of the accuracy of thecurrent estimate with the accuracy of one or more recent estimates.Alternatively, the delay period may be calculated based on a comparisonof the accuracy of the current estimate with a predetermined accuracy oraccuracy range, such as +/−2%. For example, in an embodiment apredetermined accuracy may be subtracted from the accuracy of thecurrent estimate to determine an accuracy difference. This differencemay then be used to calculate the delay period. For example, if theaccuracy of the current estimate is +/−8% and the acceptable range is+/−2%, the difference may be calculated to be +/−6%. The difference maythen be multiplied by some factor such as 2 seconds/% to determine thedelay period (in this example, 12 seconds). Other algorithms are alsopossible and any mathematical algorithm for calculating a delay periodbased on an accuracy or a difference between accuracies may be used.

Alternatively, the delay period may be selected from one or morepredetermined delay periods based on the calculated accuracy. Forexample, if a calculated accuracy is between the range +/−6% to +/−8%,then a predetermined delay period of 5 seconds may be used.

After waiting for the delay period, the method 800 illustrates a thirddetermination operation 814 in which the alarm condition is testedagain. The third determination operation 814 tests a newly estimatedvalue and thus includes receiving additional data, calculating a newestimated value and, optionally, calculating a new accuracy. Thus, thethird determination operation 814 could be considered to includerepeating the receiving data operation 802, the calculate estimatedvalue operation 804 and the calculate accuracy operation 806 as well asany operations (such as those shown in FIG. 7) necessary to determineand/or adjust the threshold.

If the third determination operation 814 determines that the thresholdis no longer exceeded, the method returns to its normal monitoring stateas illustrated by the operational flow returning to the receive dataoperation 802. On the other hand, if the third determination operation814 determines that the threshold is still exceeded, a generate alarmoperation 816 is performed.

In an alternative embodiment, the third determination operation 814 maybe performed periodically during the delay period to determine if,during the delay period, the estimated value no longer exceeds thethreshold or if the accuracy increases or decreases further. If in adetermination operation 814 performed during the delay period it isdetermined that the estimated value no longer exceeds the threshold,then the method may return to its normal monitoring mode withoutgenerating an alarm. Alternatively, if the accuracy of the currentestimates are determined to have degraded further, then the delay periodmay be recalculated in response.

The generate alarm operation 816 includes generating some form ofnotification, such as an audio alarm, a visual alarm, or an electronicmessage, that indicates that the threshold has been exceeded. Asdescribed above, an alarm may be generated until the measured dataindicate that the adjusted alarm condition is no longer met, until auser resets the device, or until some other interrupt occurs.

As mentioned above, the methods described in FIGS. 7 and 8 could becombined to create yet another embodiment of a method for generating analarm in which an adjusted threshold is calculated and if that thresholdis exceeded, a delay period is used to delay the generation of an alarm.

FIG. 9 is a block diagram illustrating some of the components of a pulseoximetry system that generates an alarm based the accuracy and/orprobability distribution of sensed data. In the embodiment shown, thesystem 900 includes a sensor 902 containing a light emitter 904 and alight detector 906; and, in a separate housing 918, a processor 908, analarm module 910, a probability distribution module 920, an oxygensaturation module 912, an accuracy module 918 and a memory 914. Adisplay 916 is also provided. The sensor 902 and its components operateas described previously with reference to FIG. 2.

The memory 914 may include RAM, flash memory or hard disk data storagedevices. The memory stores data, which may be filtered or unfiltereddata, received from the detector 906. The data may be decimated,compressed or otherwise modified prior to storing in the memory 914 inorder to increase the time over which data may be retained. In addition,the memory 914 may also store one or more of the predeterminedthreshold, the adjusted threshold, at least one estimated value ofoxygen saturation of a patient's blood, at least one accuracy associatedwith an estimated value, the probability distribution, and a delay timeperiod calculated based on at least one accuracy of an estimated value.

The oxygen saturation module 912 generates a current oxygen saturationmeasurement from the data generated by the sensor. The probabilitydistribution module 920 performs the analyses of the data received bythe oximeter 918 and calculates the probability distribution for use inadjusting the alarm threshold. In an embodiment, the probabilitydistribution module 920 is capable of calculating one or more parameterssuch as an upper confidence limit, a lower confidence limit, and aconfidence interval based on information received from the sensor and astored confidence level.

In the embodiment shown, a separate accuracy module 918 is illustrated.The accuracy module 918 is capable of calculating the accuracy of theestimated value of the oxygen saturation.

An alarm module 910 is further illustrated. The alarm module 910 teststhe alarm condition and, if the conditions are met, generates an alarmas described above. The alarm module 910 is capable of generating analarm based on the estimated value and a predetermined alarm threshold.In an embodiment, the alarm module is further capable of delaying thegeneration of an alarm based on the accuracy of the estimated value. Inyet another embodiment, the alarm module is further capable ofcalculating an adjusted threshold based on the probability distributionand the predetermined threshold and then generating an alarm based onthe adjusted threshold.

In an embodiment, the oxygen saturation module 912, alarm module 910,probability distribution module 920 and accuracy module 918 may each bea dedicated hardware circuit that may include filters, firmwarecomprising lookup tables or other data, and its own processor (notshown) that allow it to generate the current oxygen saturationmeasurement. In an alternative embodiment, they may be implemented as asingle software application or separate software applications that areexecuted, in whole or in part, by the system processor 908. In yetanother embodiment, functions described herein as being performed by theoxygen saturation engine and modules may be distributed among hardware,software and firmware throughout the system 900 and its othercomponents.

The display 916 may be any device that is capable of generating anaudible or visual notification. The display need not be integrated intothe other components of the system 900 and could be a wireless device oreven a monitor on a general purpose computing device (not shown) thatreceives email or other transmitted notifications from the oximeter 918.

It will be clear that the described systems and methods are well adaptedto attain the ends and advantages mentioned as well as those inherenttherein. Those skilled in the art will recognize that the methods andsystems described within this specification may be implemented in manydifferent manners and as such is not to be limited by the foregoingexemplified embodiments and examples. In other words, functionalelements being performed by a single or multiple components, in variouscombinations of hardware and software, and individual functions can bedistributed among software applications and even different hardwareplatforms. In this regard, any number of the features of the differentembodiments described herein may be combined into one single embodimentand alternate embodiments having fewer than or more than all of thefeatures herein described are possible.

While various embodiments have been described for purposes of thisdisclosure, various changes and modifications may be made which are wellwithin the scope of the described technology. Numerous other changes maybe made which will readily suggest themselves to those skilled in theart and which are encompassed in the spirit of the invention disclosedand as defined in the appended claims.

1. A method for generating an alarm indicating that a physiologicalparameter has exceeded a predetermined threshold, the method comprising:calculating a probability distribution of an actual value of thephysiological parameter based at least in part upon data; identifyingthe predetermined threshold associated with the physiological parameter;and generating the alarm based at least in part upon the predeterminedthreshold and the probability distribution.
 2. The method of claim 1further comprising: calculating an estimated value of the physiologicalparameter based at least in part upon data; calculating an adjustedthreshold based at least in part upon the predetermined threshold andthe probability distribution; and comparing the estimated value of thephysiological parameter to the adjusted threshold.
 3. The method ofclaim 1 further comprising: calculating, based at least in part upon theprobability distribution, a confidence interval for the actual value ofthe physiological parameter; and comparing the confidence interval tothe predetermined threshold.
 4. The method of claim 3 furthercomprising: retrieving a confidence level; and calculating, based atleast in part upon the probability distribution, the confidence intervalfor the physiological parameter using the retrieved confidence level. 5.The method of claim 4, wherein the retrieved confidence level isselected from 99%, 98%, 97.5%, 95%, 90% and a user-selected value. 6.The method of claim 2, wherein calculating the probability distributionfurther comprising: calculating an accuracy of the estimated value basedat least in part upon the data.
 7. The method of claim 6, whereincalculating the probability distribution further comprising: calculatingthe probability distribution of the actual value of the physiologicalparameter based at least in part upon the accuracy and the estimatedvalue.
 8. The method of claim 7, wherein calculating the probabilitydistribution further comprising: retrieving, based at least in part uponthe accuracy and the estimated value, a confidence limit value from aset of predetermined confidence limit values; and comparing theconfidence limit value to the predetermined threshold.
 9. A method forgenerating an alarm indicating that a physiological parameter hasexceeded a predetermined threshold, the method comprising: calculating afirst estimated value of the physiological parameter and a firstaccuracy of the first estimated value based at least in part upon data;determining that the first estimated value of the physiologicalparameter exceeds the predetermined threshold; and delaying generatingthe alarm based on the first accuracy.
 10. The method of claim 9 furthercomprising: calculating a delay time period based at least in part upona comparison of the first accuracy to a previously determined accuracyassociated with a previously estimated value of the physiologicalparameter.
 11. The method of claim 10 further comprising: after thedelay time period, calculating a second estimated value of thephysiological parameter based at least in part upon new data; and if thesecond estimated value of the physiological parameter still exceeds thepredetermined threshold, generating the alarm.
 12. The method of claim 9further comprising: determining that the first accuracy exceeds apredetermined accuracy range.
 13. The method of claim 9 furthercomprising: after a predetermined time period, calculating a secondestimated value of the physiological parameter and a second accuracy ofthe second estimated value based at least in part upon new data;determining that the second accuracy does not exceed the predeterminedaccuracy range and the second estimated value of the physiologicalparameter exceeds the predetermined threshold, generating the alarm. 14.The method of claim 12, further comprising: prior to calculating thefirst estimated value, calculating a third estimated value of thephysiological parameter and a third accuracy of the current estimatedvalue from data; determining that the third estimated value of thephysiological parameter does not exceed the predetermined threshold; andsetting the third accuracy as the predetermined accuracy range.
 15. Apulse oximeter comprising: an oxygen saturation module capable ofcalculating an estimated value of pulse rate or oxygen saturation of apatient's blood from information received from a sensor; an accuracymodule capable of calculating the accuracy of the estimated value; analarm module capable of generating an alarm based at least in part uponthe estimated value and a predetermined alarm threshold; and wherein thealarm module is capable of delaying the generation of an alarm based atleast in part upon the accuracy of the estimated value.
 16. The pulseoximeter of claim 15, further comprising: a probability distributionmodule capable of calculating a probability distribution of an actualvalue of the oxygen saturation of a patient's blood based at least inpart upon information received from the sensor; and wherein the alarmmodule is further capable of calculating an adjusted threshold based atleast in part upon the probability distribution and the predeterminedthreshold and generating an alarm based at least in part upon theadjusted threshold.
 17. The pulse oximeter of claim 16, furthercomprising: a processor capable of executing software applications; andwherein at least one of the modules is a software application capable ofbeing executed by the processor.
 18. The pulse oximeter of claim 16,further comprising: a memory capable of storing one or more of thepredetermined threshold, the adjusted threshold, at least one estimatedvalue of oxygen saturation of a patient's blood; at least one accuracyassociated with an estimated value, the probability distribution, and adelay time period calculated based on at least one accuracy of anestimated value.